{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb00c7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3817185f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Figure 4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbb30338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "#plt.hlines(0.123472, 16, 177, color='red', linestyle='-', linewidth=1,zorder=11)\n",
    "#plt.hlines(0.153472, 16, 177, color='blue', linestyle='--', linewidth=1,zorder=11, alpha=0.7)\n",
    "#plt.hlines(0.090, 16, 177, color='blue', linestyle='--', linewidth=1,zorder=11, alpha=0.7)\n",
    "\n",
    "#plt.hlines(0.054225, 201, 422, color='red', linestyle='-', linewidth=1,zorder=11)\n",
    "#plt.hlines(0.035, 201, 422, color='blue', linestyle='--', linewidth=1,zorder=11, alpha=0.7)\n",
    "#plt.hlines(0.073, 201, 422, color='blue', linestyle='--', linewidth=1,zorder=11, alpha=0.7)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"CV\"], color = 'white', s = 50, alpha=1,edgecolor='black',zorder=10)\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ 0, 0.05, 0.1, 0.15,0.2], labels = ['','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 0.2])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('CV.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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